Bayesian analysis of extreme value regression

Abstract

The article takes up Bayesian inference in extreme value distributions and also considers extreme value regression, which appears relatively uncommon in the regression literature. Numerical methods are organized around Gibbs sampling. It is shown that simple and reliable numerical techniques can be devised by exploiting the particular form of the posterior conditional distributions. The sampling behaviour of the proposed estimators is also explored via Monte Carlo simulation.

Keywords:

• extreme value regression
• MCMC
• Bayesian inference
• extreme value distribution

JEL Classification:

• C01
• C11
• C16
• C46
• C63
• G10

Acknowledgements

This research project is co-financed by EU–European Social Fund (80%) and the Greek Ministry of Development – GSRT (20%). Research support from ‘Basic Reasearch Funding Program’, BRFP-AUEB 2010, 2011 is gratefully acknowledged. Other research supports are also gratefully acknowledged.

Notes

¹Details on the properties of this distribution are provided upon request.